Discriminating clear sky from clouds with MODIS

The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. It will be produced globally at single-pixel resolution; the algorithm uses as many as 14 of t he MODIS 36 spectral bands to maximize reliable cloud detection and to miti gate past difficulties experienced by sensors with coarser spatial resoluti on or fewer spectral bands. The MODIS cloud mask is ancillary input to MODI S land, ocean, and atmosphere science algorithms to suggest processing opti ons. The MODIS cloud mask algorithm will operate in near real time in a lim ited computer processing and storage facility with simple easy-to-follow al gorithm paths. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a serie s of threshold tests attempts to detect the presence of clouds in the instr ument field of view. Each cloud detection test returns a confidence level t hat the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions r elies on different tests. Tests capable of detecting similar cloud conditio ns are grouped together. While these groups are arranged so that independen ce between them is maximized, few, if any, spectral tests are completely in dependent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of par ticular cloud types. The product of all the group confidences is used to de termine the confidence of finding clear-sky conditions. This paper outlines the MODIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MODIS cloud mask, initial validation of some of the individual cloud tests is presented using existi ng remote sensing data sets.